FLAGSHIP
Edge AI & GPU
Serving vision and language models on real hardware at the edge.
- Fail-closed computer vision on single-node OpenShift - probes that catch a silently dead model, not just a dead pod.
- One GPU, two workloads - a single card time-sliced between LLM serving and video transcode instead of reserving separate hardware.
- Vendor video-analytics stacks re-platformed as first-class Kubernetes workloads, not appliances.
- Local models behind an OpenAI-compatible gateway - swap the backend, keep every client.
- NVIDIA GPU Operator
- GPU passthrough (ESXi, as code)
- GPU readiness probing & watchdogs
- Pod lifecycle management
- YOLO / computer-vision inference
- Local LLM serving (llama.cpp)
- Edge Kubernetes
